An example of conventional object pose estimation systems (hereafter, referred as the “conventional technique”) is disclosed in Japanese Laid-Open Patent Publication No. 2003-058896. As shown in FIG. 1, this conventional object pose estimation system is composed of an image input unit 10, a three-dimensional object model storage unit 45, and an pose estimation unit 25. The pose estimation unit 25 includes an pose candidate decision unit 30, a comparison image generation unit 40, a difference calculator 55, and a determination unit 56.
The conventional object pose estimation system configured in this manner operates as described below. The three-dimensional object model storage unit 45 prestores a plurality of three-dimensional object models generated by measuring a plurality of objects. The pose estimation unit 25 performs pose estimation by comparing an input image input from the image input unit 10 with a three-dimensional object model retrieved from the three-dimensional object model storage unit 45.
Specifically, the pose candidate decision unit 30 first generates a plurality of pose candidates and outputs them to the comparison image generation unit 40. According to the generated pose candidates, the comparison image generation unit 40 generates a plurality of comparison images whose illumination conditions are close to those of the input image, while projecting the three-dimensional object model to a two-dimensional image, and outputs the generated comparison images to the difference calculator 55. The difference calculator 55 compares the input image from the image input unit 10 with the comparison images from the comparison image generation unit 40 to calculate a difference for each of the comparison images, and outputs the calculated differences to the determination unit 56. The determination unit 56 selects a comparison image closest to the input image from among the plurality of comparison images based on the calculated differences to estimate an optimal pose, and outputs the estimation result.
The conventional techniques described above have problems as follows. Even if the object of the three-dimensional object model is the same as the object of the input image, the difference between the input image and the comparison image will sometimes become small enough (or the similarity will become great enough) to lead to erroneous pose estimation, if the pose estimated from the three-dimensional object model does not match the object pose of the input image. Even if the object of the three-dimensional object model is different from the object of the input image, the difference between the input image and the comparison image will sometimes become small enough to lead to erroneous pose estimation.
This is for the reason that, according to the conventional technique, pose estimation is performed by generating several comparison images close to the input image from the three-dimensional object model, and selecting a comparison image closest to the input image by only the comparison between the input image and the several comparison images. This means that, if the pose estimated from the three-dimensional object model does not match the object pose of the input image, the difference becomes relatively high since positions of edges or the like are different when the generated comparison images have similar sharpness to that of the input image. However, if the generated comparison images have lower sharpness than that of the input image, the error at the edges of the comparison images is reduced. In this case, the difference between the input image and the comparison images becomes relatively small. As a result, the difference between the input image and a comparison image at a wrong pose may become the smallest due to slight noise or the like.
When the object comparison is performed, the comparison is executed by selecting an object having the smallest difference between an optimal comparison image obtained from the three-dimensional object models and the input image. However, if the difference is found only by the comparison between the input image and the comparison images, the difference may become small enough even for a wrong object to lead to an erroneous comparison result.
It is therefore an object of the present invention to provide an object pose estimation and comparison system, and an object pose estimation and comparison method which are capable of performing highly precise pose estimation and comparison on object images taken at various poses and under various illumination conditions.
It is another object of the present invention to provide an object comparison system and an object comparison method which are capable of performing highly precise comparison on object images taken at various poses and under various illumination conditions.